Electrical Equipment Research Articles

Computer Simulation and Simulation of Mechanical and Electrical Equipment based on Artificial Intelligence Algorithms

The computer in mechanical and electrical equipment can now detect equipment faults through simulation thanks to the advancement of artificial intelligence (AI) technology, which makes it convenient to monitor mechanical and electrical equipment. This paper begins with a quick introduction to Agent and the Agent system, explains how Agent is applied in the current context, then analyzes the hierarchical fault diagnostic model, identifies its flaws, and suggests improvement techniques. To increase the model’s accuracy and speed of operation, the contract net model and D-S (Dempster-Shafer) evidence theory are then incorporated. Finally, simulation experiments are used to confirm the accuracy and consistency of this model. According to the experimental findings, this optimization model runs at a pace that is noticeably faster than that of other models when subjected to the same workload, demonstrating the model’s efficacy. Models 1, 2, and 3 are put side by side to demonstrate how clearly multi-task processing may cut down on the model’s running time. In the second experiment, samples of mechanical and electrical equipment defect data are taken from two groups. The results of the comparative experiments demonstrate that the optimized model in this work can be reliable up to a maximum of 0.91 and a minimum of 0.63. It is demonstrated that the model in this work is rational by the fact that among the four types of fault prediction, the optimized model’s reliability is significantly greater than the traditional model’s. The research described in this publication therefore has some reference value for computer simulation of electromechanical equipment.

Potential and Challenges in Adapting Green Behavior Towards Energy-Efficient Educational Building (Case Study: i-CELL FTUI)

Abstract The amount of energy consumption in the building sector has led to the concept of green buildings. This concept is more familiar with focusing on technical aspects, where, in fact, the factor that causes the amount of energy consumption comes from the human factor. This paper aims to examine green behavior in the consumption of electrical energy in green buildings. The case study used is the FTUI i-CELL Building, which is a green building. The method used is a literature study and a questionnaire to find the potential for behavior change, savings, and challenges or obstacles from the perspective of users and managers. Based on the study’s results, the building users’ role can provide potential savings in the coverage of cooling, lighting, and electrical equipment that is greater than that of the manager. it was found that green behavior by users of the building could save 26.60%-36.55% of the energy consumption, whereas behavior by building managers only saved 8.73%-23.67%. Even so, both of them still have to work together to achieve savings in electricity consumption through green behavior. This is because the manager has an important role, such as equipment maintenance, which can affect electricity consumption. In addition, in achieving potential savings, both users and managers experience challenges or obstacles in terms of knowledge, norms, and perceptions of control that shape green behavior in electricity consumption. Suggestions for further development are adding simulation to determine the amount of savings and identify the correlation and significance of knowledge, norms, and perceptions of control that play a role in shaping green behavior.

Probabilistic seismic risk analysis of electrical substations considering equipment-to-equipment seismic failure correlations

When an earthquake occurs, electrical equipment in a substation exhibits a certain level of seismic failure correlation since they suffer similar ground motions and share similar structural characteristics. However, this equipment-to-equipment seismic failure correlation (E2ESFC) was neglected in previous substation-level probabilistic seismic risk analyses due to the lack of awareness and practical approach. To investigate the effect of different degrees of the E2ESFC on the substation seismic risk, an efficient method for considering partially correlated seismic failure was proposed. The concepts of “damage demand probability” and “damage capacity probability” were derived from the equipment's fragility curve. Then the partial correlation of equipment's capacity probabilities can be easily introduced and incorporated into the substation-level risk analysis through the combination of Copula functions and the Monte Carlo simulation. A case study on a real-world 220/110 kV substation using an equi-correlation model demonstrated that ignoring the E2ESFC among the same type of equipment will lead to an underestimate of the probability of seeing high seismic loss. Furthermore, a general method to assess the E2ESFC coefficients between equipment was also proposed, laying the foundation to facilitate applications of the introduced E2ESFC simulation method and to generate a more reliable system risk assessment result.

Indoor air quality improvement using developed polymeric membrane filter

Abstract Carbon dioxide (CO2) levels must be controlled to protect the environment and human health. Previous studies focused on improving indoor air quality (IAQ) by reducing these negative impacts with electrical equipment such as mechanical ventilators and air purifiers; however, passive techniques were lacking. This work develops a prototype membrane filters to improve IAQ by capturing and separating indoor CO2 levels. These filters use polyvinyl alcohol (PVA) membranes and activated carbon (AC) as innovative and effective passive materials at four concentrations. Scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), and X-Ray diffraction (XRD) were employed to characterize the materials. The effectiveness of CO2 absorption across the four filter types was then assessed through tests conducted in a controlled environment. Subsequently, site monitoring was undertaken to evaluate the most effective filter. The absorption rates of CO2 levels indoors of the developed filters are 17.5%, 8.1%, 4.5%, and 3.4%, respectively, for four filters. The most efficient filter increased absorption efficiency by 47.8% in an unoccupied space and 35.5% in an occupied one. This passive material can be applied in various indoor spaces, including office buildings, residential buildings, and educational facilities, due to its benefits in saving energy and enhancing indoor environmental quality.

Study of Pressure Fields in a Water Well during its Cleaning from Salt Deposits

The paper deals with the pressure fields in a water well during its cleaning from salt deposits using electric discharge equipment with the location of the charging and discharging device on the Earth surface. The curves of the spatial pressure distribution in a water well were obtained. Those graphs could be used to determine the processing modes, namely the number of processing points per 1 linear meter of the well. The results obtained showed a strong influence on the cleaning efficiency of the parameters of the high-voltage cable that connects the surface and submersible parts (electrical characteristics of the cable and its length). Methods to increase the efficiency of the impact are proposed.

Regional BDE-209 emission, environmental fate and risks: Methods establishment, data filling and feature analysis

BDE-209 is an emerging environmental contaminant that poses a significant threat to human health. Despite its inclusion in the Stockholm Convention, the current regional emission levels, environmental fate, and corresponding risks remain unclear, especially with the ongoing release of BDE-209 during the disposal of waste electrical and electronic equipment. The significant gaps in BDE-209 emission data highlight the need for a regional approach to better understand these issues. Therefore, we established a method to identify regional BDE-209 emissions, environmental fate, and risks in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) using a substance flow analysis framework, fugacity model, and risk model. Our results showed: (1) Despite the increasing pressure of electronic waste, emissions have decreased with gradual strengthening of restrictions. The stage with the highest contribution to emissions is flame-retardant plastic production, which accounts for 41 % of emissions, followed by informal treatment (36 %). (2) The largest BDE-209 emissions are into air and soil, at 7.23 t and 4.56 t, respectively, and the highest reserves are in soil and sediment. (3) Infants and young children have the highest levels of exposure and cancer risk. This research helps fill the multi-regional data gap for BDE-209 and clarify the complex regional emission situation in the GBA.

Automated monitoring of insulation by ultraviolet imaging employing deep learning

The corona effect on the surface of electrical system equipment and components generally indicates undesirable phenomena that can lead to physical degradation of materials or even equipment failure. One of the most promising techniques for monitoring corona discharges is the use of specialized cameras for the detection of ultraviolet radiation. This paper introduces an innovative algorithm for classifying the criticality of insulation based on attributes extracted from videos recorded using an ultraviolet detection camera. The attributes extracted from each facula origin include maximum persistence, area, and the minimum distance between the facula origin and the insulation. To obtain this distance, a technique combining a deep convolutional neural network model with an adaptive segmentation thresholding method is proposed. To validate the proposed methodology, inspections were conducted at a 500 kV substation. A total of 96 videos were recorded, within which 99 facula origins were identified. The object detection model applied demonstrated an accuracy of 85.5 % in detecting insulation in images, based on a validation set comprising 1,985 images and 8,730 instances. The results of the classification showed that 72.7 % of the facula origins recorded originated from regions far from the insulation (mainly cables and corona rings). These results demonstrate that the distance between the insulation and the facula origin is an essential attribute for video analysis, providing context for recorded discharges and allowing differentiation between cases where ultraviolet radiation originates from insulation and those where discharge location is less critical.

E-Waste Management Practices Through the Eyes of Responsible Departments at Malaysian Public Universities

Electronic and electrical waste (E-waste) has been identified as the fastest-growing waste stream in the world. In Malaysia, forecasts indicate that it will soon reach 24.5 million units of e-waste by the year 2025. Today, higher learning institutions also contribute to the rapidly growing threat of e-waste. Besides mobile phones, the usage of Information and Communication Technology (ICT) equipment at universities keeps increasing and contributes to the increased quantity of e-waste, while the handling and disposal methods of e-waste still need to be improved. The purpose of this paper is to examine the current e-waste management system as well as to investigate the critical challenges constraining e-waste management at public universities in Malaysia. By using a qualitative approach, a number of interview sessions have been conducted with the responsible departments at six established public universities in Malaysia. It has been found that currently only e-waste generated from used electric and electronic equipment (EEE) which have been declared as university assets are being managed by the responsible departments at the six universities. All six universities are using the standard process of handling their e-waste since every single process needs to comply with the requirements of the Ministry of Finance (MOF). This remains the main constraint to flexibility or improvement of the current system. The universities are also facing some challenges, among others, lack of awareness and knowledge on how to properly handle e-waste among university citizens, no clear sustainable policy at the university, lack of facilities within the university compound to support the proper practice of e-waste disposal as well as lack of education and related training provided to the campus citizens. This paper also provides some recommendations for the improvement of e-waste management towards sustainability and offers an agenda for future research.

An approach to formalization of WEEE informal collectors: Collection model under a membership-based community

Informal collectors’ formalization has become a key challenge that developing countries such as China face in governing informal recycling. This study establishes an innovative collection model of waste electrical and electronic equipment (WEEE) under the membership community operation system, which integrates waste merchants into the formal recycling system in the form of members. Based on a Bass model of membership community development and an evolutionary game model between recyclers and waste merchants, respectively, this study uses system dynamics theory to simulate and analyze the model’s implementation effectiveness and influencing factors. We find that the membership community’s interpersonal promotion and customer relationship management strategies can increase waste merchants’ membership rate and membership loyalty, which is an effective way to incorporate waste merchants into the formal recycling system. Waste merchants’ main interest demands in the formalization process are to gain economic benefits and identity recognition, which motivate them to become members. Membership’s transaction cost establishes a long-term cooperative relationship between waste merchants and recyclers in the form of an economic contract. However, the potential losses from information sharing are the key factor restricting waste merchants from enrolling as members, which has also become the main obstacle to their formalization. This study provides a new theoretical and empirical basis for informal collectors’ formalization, which has important practical significance for WEEE recycling and management in developing countries.

E-waste in the environment: Unveiling the sources, carcinogenic links, and sustainable management strategies

E-waste refers to electrical and electronic equipment discarded without the intent of reuse by choice or at the end of its functional lifespan. In 2022, approximately 62 billion kilograms of e-waste, equivalent to 7.8 kilograms per capita, was generated globally. With an alarming annual growth of approximately 2 million metric tonnes, e-waste production may exceed 82 billion kilograms by 2030. Improper disposal of e-waste can be detrimental to human health and the entire biosphere. E-waste encompasses a wide range of chemicals, including heavy metals, Polychlorinated Biphenyls (PCBs), Per- and Polyfluoroalkyl Substances (PFAS), Polycyclic Aromatic Hydrocarbons (PAHs), Polychlorinated Dibenzo-dioxins and -furans (PCDD/Fs), Polybrominated Diphenyl Ethers (PBDEs), and radioactive elements. Improper disposal of e-waste equipment can directly contaminate the aquatic and terrestrial environment, leading to human exposure through ingestion, inhalation, dermal absorption, and trans-placental transfer. These contaminants can directly enter the human body from the environment, potentially fueling carcinogenesis by modulating cell cycle proteins, inducing oxidative stress, and mutations. Heavy metals such as cadmium, mercury, arsenic, lead, chromium, and nickel along with organic pollutants like PAHs, PCBs, PBDEs, PFAS, and radioactive elements, play a crucial role in inducing malignancy in humans. Effective collection, sorting, proper recycling, and appropriate disposal techniques are essential in reducing environmental contamination by e-waste-derived chemicals. Hence, this comprehensive review aims to uncover the global environmental burden of e-waste and its links to carcinogenesis in humans. Furthermore, it provides an inclusive discussion on potential treatment approaches to minimize environmental e-waste contamination.

Effects of wire rope isolators on seismic life-cycle cost of UHV bypass switch

The seismic isolation devices reduce the seismic vulnerability of the electrical equipment. Accurately assessing the seismic life-cycle cost (SLCC) of the electrical equipment is beneficial in guiding the design and enhancing the seismic resilience of electrical substations and converter stations. To evaluate the effects of the isolator devices on the seismic life-cycle cost of electrical equipment, a SLCC evaluation model was proposed in this study, and the evaluation was conducted on an ultra-high voltage (UHV) bypass switch (BPS) with wire rope isolators (WRI). The model takes into account equipment purchase, maintenance, transportation and installation costs and indirect losses caused by power outages. Afterward, the SLCC and break-even time of the UHV BPS with and without WRIs in different regions were analyzed. The results indicate that beyond the break-even time, the BPS with WRIs becomes more economically viable. Moreover, its economic viability increases as the service life extends. Therefore, in high seismic cost risk areas, it is recommended to adopt seismic isolation devices to ensure the secure and economically efficient operation of electrical equipment.

MODERNIZATION OF THE WATER PURIFICATION SYSTEM FOR SEWAGE TREATMENT PLANTS USING SCHNEIDER ELECTRIC EQUIPMENT

Currently, the issue of modernization of outdated automatic control systems at strategically important infrastructure facilities of the city, such as water supply and water treatment, is urgent. Using modern automation equipment allows for improving the environmental situation and increasing the reliability of technological processes. This article discusses the development of a new automated system for the biological treatment facility of the Almaty Su sewage treatment plant to replace the outdated current automation. A control system for air conditioning machines, based on Schneider Electric equipment, has been developed using EcoStructure Control Expert software to manage the Modicon M340 controller. Additionally, using EcoStructure Machine Expert, a control system has been created for air injection machines during the biological cleaning phase, which uses the Modicon M241 controller.A process control mnemonic circuit was designed to switch between Modicon M340 and M241 controllers. Remote I/O architectures were developed using the Architecture Builder to create network configurations. An emergency protection system was implemented on Honeywell equipment at Kazakh-British Technical University (KBTU) JSC, using Safety Controller and Manager tools. Security levels at SIL enterprises were calculated.

Optimal scheduling strategy of household electrical equipment based on scenario dynamic modeling

Household electricity is highly unpredictable, necessitating a deep understanding of its impact on energy scheduling for optimal resource allocation and economic gains. This issue’s complexity can lead traditional optimization methods to converge on suboptimal solutions. In this study, the dynamic Copula (DC) model is used to construct the dynamic correlation between power consumption characteristics, and the Monte Carlo (MC) method is used to generate multiple power consumption scenarios to cope with the uncertainty of user behavior. Then, the optimal scenario set is selected through the average distribution error (ADE) to participate in the subsequent scheduling. In addition, based on the established equipment operation characteristic model, the electricity cost and load peak-to-average ratio (PAR) are comprehensively considered. The Improved Dynamic search multi-objective particle swarm optimization (IDSMOPSO) is introduced to optimize the running time of the equipment. Taking the electricity consumption of a family in Xi’an as an example, the results show that the algorithm is significantly better than the other two improved algorithms in performance. Meanwhile, the electricity cost of the family was significantly reduced by 17.09 %, and the PAR value was also reduced by 31.59 %, which realized the economic operation of household electricity.

A Novel Method for Online Diagnostic Analysis of Partial Discharge in Instrument Transformers and Surge Arresters from the Correlation of HFCT and IEC Methods

In this work, a new methodology is proposed for the online and non-invasive extraction of partial discharge (PD) pulses from raw measurement data obtained using a simplified setup. This method enables the creation of sub-windows with optimized size, each containing a single candidate PD pulse. The proposed approach integrates mathematical morphological filtering (MMF) with kurtosis, a first-order Savitzky-Golay smoothing filter, the Otsu method for thresholding, and a specific technique to associate each sub-window with the phase angle of the applied voltage waveform, enabling the construction of phase-resolved PD (PRPD) patterns. The methodology was validated against a commercial PD detection device adhering to the IEC (International Electrotechnical Commission) standard. Experimental results demonstrated that the proposed method, utilizing an off-the-shelf 8-bit resolution data acquisition system and a low-cost high-frequency current transformer (HFCT) sensor, effectively diagnoses and characterizes PD activity in high-voltage equipment, such as surge arresters and instrument transformers, even in noisy environments. It was able to characterize PD activity using only a few cycles of the applied voltage waveform and identify low amplitude PD pulses with low signal-to-noise ratio signals. Other contribution of this work is the diagnosis and fault signature obtained from a real surge arrester (SA) with a nominal voltage of 192 kV, corroborated by destructive disassembly and internal inspection of the tested equipment. This work provides a cost-effective and accurate tool for real-time PD monitoring, which can be embedded in hardware for continuous evaluation of electrical equipment integrity.

Numerical analysis of flexural behaviour of UHPC-CFTS columns as enclosure supports for electrical substations

Abstract This study establishes a finite element analysis model of ultra-high-performance concrete (UHPC) encased concrete filled steel tube (CFST) composite columns under combined compression and bending. Appropriate constitutive models and element types are selected as the basis. The finite element model simulates the test results of steel-concrete composite columns, and the applicability of the model is verified by comparison with existing test data of UHPC encased CFSTs. On the basis of demonstrating model reliability, the full-range load-deformation curves of UHPC encased CFSTs with different concrete strengths under compression-bending are compared. The failure modes and material stress distributions are analyzed to elucidate the influence mechanism of the UHPC on the flexural performance of UHPC encased CFSTs under combined compression and bending. The results show that compared to normal concrete, UHPC can effectively improve the flexural capacity of UHPC encased CFST columns. Owing to the addition of steel fibers, the failure mode of UHPC encased CFSTs under combined compression and bending is more integral compared to normal concrete. The research results of this study can provide references for the performance evaluation and design of UHPC encased CFSTs in the structural enclosures for electrical equipment in high/low voltage substations.

The Potential for High‑tech Exports from Selected EU Member States to China and the Related Opportunities Arising from the Belt and Road Initiative

The EU’s economic relations with China are characterised by a significant trade deficit. Enhancing EU exports of high-tech products, in particular, to China is the best way out of this imbalance. In doing so, EU countries can take advantage of the opportunities provided by the Belt and Road Initiative (BRI) that intends to improve connectivity between Europe and Asia. Objective: This study aims to identify the untapped potential for high-tech exports of selected EU countries in trade with China and to evaluate the opportunities arising from the BRI in utilising this potential. Research Design & Methods: To assess the overall high-tech export potential of selected EU countries to China, the export gap was calculated using the concept of revealed comparative advantages. Findings: Among the examined EU countries, Hungary and the Czech Republic were found to have the greatest high-tech export growth potential to China. The largest export gaps were recorded in electrical machinery and equipment. Implications & Recommendations: The findings of the study can be used by European businesses to adjust their export strategies. It can also be used by government institutions of the studied countries when designing their economic and trade policies, particularly vis-à-vis China and the BRI initiative, within the field of supporting infrastructure projects, as well as high-tech industries. Contribution & Value Added: The study contributes to the literature on the potential of EU export growth to China that has not been sufficiently explored yet. The novelty of the study is in identifying specific high-tech commodities with the highest potential for export to China.

An example of the application of neural networks of a simple architecture to unfocused well electrometry probes

An effective method of finding stable solutions of inverse problems of electric and induction logging along the well is proposed, which allows avoiding the influence of the resistance values of the neighboring formations on the determination of the geoelectrical parameters of the object under study. A highly efficient method was proposed for solving such an unstable inverse problem. This method is based on the application of a neural network with inverse error propagation of a simple architecture. Namely three-layer. The mathematical statement of the problem is given, both the topology of the neural network and all its parameters are described in detail. In the course of the numerical experiment, they were selected as optimal. The process of building a base for training a neural network is described in detail. Namely, how each of the examples of the learning base is built by solving a direct problem. With this cut parameter, the training for each example is chosen arbitrarily, which guarantees a comprehensive range for training the neural network. The number of examples in the training base is one hundred thousand examples. As the activation function, the sigmoid is chosen due to the fact that it is differentiable everywhere. The results of testing the written program are given. The learning rate was estimated to obtain the required small error. It is shown that this approach is stably convergent. For testing, the parameters of the layers of the cut, which are inherent to the geophysical parameters of the cuts of the Dnipro-Donetsk depression, were chosen. A complex of lateral logging sounding was chosen as the electrical logging equipment. Four-probe low-frequency induction logging equipment was chosen as induction logging equipment. Examples for induction and electrical logging are given separately. The obtained results are analyzed in detail. Ways of further improvement of the obtained neural network and its use for other problems of geophysics are given.

РАЗРАБОТКА ПРОГРАММЫ РАСЧЕТА ПАРАМЕТРОВ СХЕМЫ ЗАМЕЩЕНИЯ ТРЕХФАЗНОГО АСИНХРОННОГО ЭЛЕКТРОДВИГАТЕЛЯ

The article is devoted to developing a program for calculating the parameters of a T-shaped equivalent circuit of three-phase asynchronous electric motors. The relevance of this work is due to the need to obtain reliable results of computer simulation modelling of electric drive systems in software packages. The aim of the study is to implement in a convenient software interface an analytical technique for calculating the parameters of an asynchronous motor equivalent circuit with minimal deviations from the catalogue data. For the software calculation of the equivalent circuit parameters the paper considers analytical approximation and iterative counting techniques, since they do not require experiments on industrial electrical equipment. A comparative analysis of such techniques is performed using the example of a three-phase asynchronous motor brand BA280S4 with a power of 110 kW. The results of the compar-ative analysis show that the highest reliability of the mechanical and electromechanical characteristics is achieved in the case of using the iterative counting technique with minimizing the objective function of the sum of the weighted squares of the deviations of the control point values from the catalogue data. The authors implement the program for calculating the equivalent circuit parameters according to the selected calculation method in the Embarcadero Delphi environment. The reliable values of the electric motor parameters calculated by the program will allow for computer to model electric drive systems that best matches real objects.

BERKAH JAYA ELECTRIC SHOP APPLICATION IS BASED ON ANDROID

Berkah Jaya Electric Shop is a shop engaged in the sale of various kinds of electrical equipment. Goods sold by the store such as cables, sockets, and switches. The process of selling goods at the store is only done offline, namely by means of buyers coming directly to the store location. The process of managing store goods is still done by manually recording. This certainly causes problems such as the obstruction of buyers by time, and inefficient management of stock items. With this research, a sales application will be created that can make it easier for buyers to order goods, make product complaints, get information on the goods needed, and assist sellers in managing stock items and reports. This application will be built using the Waterfall method, utilizing the PHP, Laravel, and Kotlin programming languages. Making this application makes it easier for buyers to place orders via smartphones and sellers can more easily manage goods.

Optimized Wavelet Transform for the Development of an Algorithm Designed for the Analysis of Digital Substation Electrical Equipment Parameters

This study emphasizes the urgent need for systems that monitor the operational states of primary electrical equipment, particularly power transformers. The rapid digitalization of and increasing data volumes from substations, coupled with the inability to retrofit outdated equipment with modern sensors, underscore the necessity for algorithms that analyze the operational parameters of digital substations based on key power system metrics such as current and voltage. This research focuses on digital substations with Architecture III and aims to develop an algorithm for processing digital substation data through an appropriate mathematical tool for time-series analysis. For this purpose, the fast discrete wavelet transform was chosen as the most suitable method. Within the framework of the research, possible transformer faults were divided into two categories by the nature of their manifestation. A mathematical model for two internal transformer fault categories was built. The most effective parameters from the point of view of the possibility of identifying an internal fault were selected. The proposed algorithm shows its effectiveness in the compact representation of the signal and compression of the time series of the parameter to be monitored.